Dataset with 200 patients and severe class imbalance (2% event rate), representing extremely rare outcomes where PPV/NPV are critical metrics.
Format
A data frame with 200 rows and 3 variables:
- patient_id
Character: Patient identifier (PT001-PT200)
- rare_outcome
Factor: "Event" or "No_Event" (2%/98% prevalence)
- predictor
Numeric: Predictor value (mean: 90 for event, 50 for no event)
Details
Extreme class imbalance (2% events) with good predictor discrimination. Tests handling of severely imbalanced data and emphasis on predictive values over sensitivity/specificity.
Examples
data(psychopdaROC_imbalanced)
psychopdaROC(data = psychopdaROC_imbalanced, dependentVars = "predictor",
classVar = "rare_outcome", positiveClass = "Event",
refVar = "predictor")
#> Multiple optimal cutpoints found, applying break_ties.
#>
#> ADVANCED ROC ANALYSIS
#>
#>
#>
#>
#> Procedure Notes
#>
#>
#>
#> The ROC analysis has been completed using the following
#> specifications:
#>
#>
#>
#> Measure Variable(s): predictor
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#> Class Variable: rare_outcome
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#> Positive Class: Event
#>
#>
#>
#> Method: maximize_metric
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#> All Observed Cutpoints: FALSE
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#> Metric: youden
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#> Direction (relative to cutpoint): >=
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#> Tie Breakers: mean
#>
#> Metric Tolerance: 0.05
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#>
#>
#> <hr />
#>
#> <div style='padding: 10px; background-color: #f8f9fa; border: 1px
#> solid #dee2e6; border-radius: 4px; margin-bottom: 15px;'>
#>
#> Analysis Status
#>
#> Seed: 123Positive Class: Event (Prevalence: 2%)Analysis Mode:
#> Basic<div style='background-color: #fff3cd; color: #856404; padding:
#> 10px; border-radius: 4px; margin-top: 10px;'>Warnings:Class imbalance
#> detected (Prevalence: 2.0%). Consider using Precision-Recall curves.
#>
#> ROC Analysis Summary
#> ────────────────────────────────────────────────────────────────────────
#> Variable AUC 95% CI Lower 95% CI Upper p-value
#> ────────────────────────────────────────────────────────────────────────
#> predictor 0.9553571 0.9235097 0.9872046 < .0000001
#> ────────────────────────────────────────────────────────────────────────
#> Note. AUC 95% confidence intervals computed using the DeLong
#> method.
#>
#>
#> Clinical Interpretation
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Test Performance Level Clinical Recommendation Detailed Interpretation
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> predictor Excellent Suitable for clinical use with appropriate cutpoint The test 'predictor' has an AUC of 0.955 indicating excellent discriminatory ability. This test can reliably distinguish between diseased and healthy patients.
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> OPTIMAL CUTPOINTS AND PERFORMANCE
#>
#> no title
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Cutpoint Sensitivity Specificity PPV NPV Youden's J AUC Metric Score
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> 75.3373183 100.00000 91.32653 19.04762 100.00000 0.9132653 0.9553571 0.9132653
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Area Under the ROC Curve
#> ────────────────────────────────────────────────────────────────────────
#> Variable AUC 95% CI Lower 95% CI Upper p-value
#> ────────────────────────────────────────────────────────────────────────
#> predictor 0.9553571 0.9235097 0.9872046 < .0000001
#> ────────────────────────────────────────────────────────────────────────
#> Note. AUC 95% confidence intervals computed using the DeLong
#> method.
#>
